


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class        A     (		pl.LightningModule	):







     '''simple docstring'''







     def __init__(				self    :							Optional[Any]			,				_UpperCamelCase    :							Optional[int]):
      super().__init__()
      _lowercase:       Tuple						=				model
      _lowercase:       int						=				2
      _lowercase:       List[str]						=				nn.Linear(self.model.config.hidden_size			,				self.num_labels)






     def 					UpperCAmelCase__       (				self    :							Optional[int]):
      pass






def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__      ):
 # load longformer model from model identifier
 _lowercase:       List[Any]						=				LongformerModel.from_pretrained(__magic_name__      )
 _lowercase:       Optional[Any]						=				LightningModel(__magic_name__      )

 _lowercase:       int						=				torch.load(__magic_name__ ,   map_location=torch.device("cpu"      )      )
 lightning_model.load_state_dict(ckpt["state_dict"]      )

 # init longformer question answering model
 _lowercase:       Union[str, Any]						=				LongformerForQuestionAnswering.from_pretrained(__magic_name__      )

 # transfer weights
 longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()      )
 longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()      )
 longformer_for_qa.eval()

 # save model
 longformer_for_qa.save_pretrained(__magic_name__      )

 print(f"Conversion successful. Model saved under {pytorch_dump_folder_path}"      )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE     :			List[Any]        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--longformer_model',
           default=None,
           type=str,
           required=True,
           help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
       )
       parser.add_argument(
           '--longformer_question_answering_ckpt_path',
           default=None,
           type=str,
           required=True,
           help='Path the official PyTorch Lightning Checkpoint.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       _SCREAMING_SNAKE_CASE     :			str        =							parser.parse_args()
       convert_longformer_qa_checkpoint_to_pytorch(
           args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
       )


"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE__:int									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        warnings.warn(
            "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
            " Please use DeformableDetrImageProcessor instead."    ,		lowerCamelCase    ,		)
        super().__init__(*lowerCamelCase    ,		**lowerCamelCase						)
